首页|Memorial Sloan-Kettering Cancer Center Reports Findings in Personalized Medicine (Optimizing Sample Size for Supervised Machine Learning with Bulk Transcriptomi c Sequencing: A Learning Curve Approach)
Memorial Sloan-Kettering Cancer Center Reports Findings in Personalized Medicine (Optimizing Sample Size for Supervised Machine Learning with Bulk Transcriptomi c Sequencing: A Learning Curve Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Drugs and Therapies - Personalized Medicine is the subject of a report. According to news reporting or iginating from New York City, New York, by NewsRx correspondents, research state d, “Accurate sample classification using transcriptomics data is crucial for adv ancing personalized medicine. Achieving this goal necessitates determining a sui table sample size that ensures adequate statistical power without undue resource allocation.” Our news editors obtained a quote from the research from Memorial Sloan-Ketterin g Cancer Center, “Current sample size calculation methods rely on assumptions an d algorithms that may not align with supervised machine learning techniques for sample classification. Addressing this critical methodological gap, we present a novel computational approach that establishes the power-versus-sample-size rela tionship by employing a data augmentation strategy followed by fitting a learnin g curve. We comprehensively evaluated its performance for microRNA and RNA seque ncing data, considering diverse data characteristics and algorithm configuration s, based on a spectrum of evaluation metrics. To foster accessibility and reprod ucibility, the Python and R code for implementing our approach is available on G itHub.”
New York CityNew YorkUnited StatesNorth and Central AmericaCyborgsDrugs and TherapiesEmerging TechnologiesMachine LearningPersonalized MedicinePersonalized Therapy
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